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from fastai.collab import *   # Quick access to collab filtering functionality

Collaborative filtering example

collab models use data in a DataFrame of user, items, and ratings.



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path = untar_data(URLs.ML_SAMPLE)
path









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PosixPath('/home/sgugger/.fastai/data/movie_lens_sample')



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ratings = pd.read_csv(path/'ratings.csv')
series2cat(ratings, 'userId', 'movieId')
ratings.head()



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data = CollabDataBunch.from_df(ratings, seed=42)



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y_range = [0, 5.5]

That's all we need to create and train a model:



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learn = collab_learner(data, n_factors=50, y_range=y_range)
learn.fit_one_cycle(4, 5e-3)



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learn.predict(ratings.iloc[0])









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(FloatItem 4.145714, tensor(4.1457), tensor(4.1457))



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	userId	movieId	rating	timestamp
0	73	1097	4.0	1255504951
1	561	924	3.5	1172695223
2	157	260	3.5	1291598691
3	358	1210	5.0	957481884
4	130	316	2.0	1138999234

epoch	train_loss	valid_loss	time
0	1.785755	1.323691	00:01
1	0.907917	0.682382	00:01
2	0.668087	0.658977	00:01
3	0.573576	0.650910	00:01